Mitigating Annotation Artifacts in Natural Language Inference Datasets to Improve Cross-dataset Generalization Ability

by   Guanhua Zhang, et al.

Natural language inference (NLI) aims at predicting the relationship between a given pair of premise and hypothesis. However, several works have found that there widely exists a bias pattern called annotation artifacts in NLI datasets, making it possible to identify the label only by looking at the hypothesis. This irregularity makes the evaluation results over-estimated and affects models' generalization ability. In this paper, we consider a more trust-worthy setting, i.e., cross-dataset evaluation. We explore the impacts of annotation artifacts in cross-dataset testing. Furthermore, we propose a training framework to mitigate the impacts of the bias pattern. Experimental results demonstrate that our methods can alleviate the negative effect of the artifacts and improve the generalization ability of models.


page 1

page 2

page 3

page 4


Reliable Evaluations for Natural Language Inference based on a Unified Cross-dataset Benchmark

Recent studies show that crowd-sourced Natural Language Inference (NLI) ...

Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets

Natural Language Sentence Matching (NLSM) has gained substantial attenti...

MedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical Domain

Crowdworker-constructed natural language inference (NLI) datasets have b...

Misleading Failures of Partial-input Baselines

Recent work establishes dataset difficulty and removes annotation artifa...

CausalAPM: Generalizable Literal Disentanglement for NLU Debiasing

Dataset bias, i.e., the over-reliance on dataset-specific literal heuris...

HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference

Many recent studies have shown that for models trained on datasets for n...

Please sign up or login with your details

Forgot password? Click here to reset